Estimation of a Joint Distribution Function with Multivariate Interval-Censored Data when the Nonparametric MLE is not Unique

Abstract

A nonparametric estimator of a joint distribution function F0 of a d-dimensional random vector with interval-censored (IC) data is the generalized maximum likelihood estimator (GMLE), where d ≥ 2. The GMLE of F0 with univariate IC data is uniquely defined at each follow-up time. However, this is no longer true in general with multivariate IC data as demonstrated by a data set from an eye study. How to estimate the survival function and the covariance matrix of the estimator in such a case is a new practical issue in analyzing IC data. We propose a procedure in such a situation and apply it to the data set from the eye study. Our method always results in a GMLE with a nonsingular sample information matrix. We also give a theoretical justification for such a procedure. Extension of our procedure to Cox's regression model is also mentioned.